# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied.  See the License for the
# specific language governing permissions and limitations
# under the License.
"""Loads datasets, dashboards and slices in a new superset instance"""
# pylint: disable=C,R,W
import json
import os
import textwrap

import pandas as pd
from sqlalchemy import DateTime, String
from sqlalchemy.sql import column

from superset import db
from superset.connectors.sqla.models import SqlMetric
from superset.utils import core as utils
from .helpers import (
    config,
    Dash,
    DATA_FOLDER,
    get_example_data,
    get_slice_json,
    merge_slice,
    misc_dash_slices,
    Slice,
    TBL,
    update_slice_ids,
)


def load_world_bank_health_n_pop():
    """Loads the world bank health dataset, slices and a dashboard"""
    tbl_name = 'wb_health_population'
    data = get_example_data('countries.json.gz')
    pdf = pd.read_json(data)
    pdf.columns = [col.replace('.', '_') for col in pdf.columns]
    pdf.year = pd.to_datetime(pdf.year)
    pdf.to_sql(
        tbl_name,
        db.engine,
        if_exists='replace',
        chunksize=50,
        dtype={
            'year': DateTime(),
            'country_code': String(3),
            'country_name': String(255),
            'region': String(255),
        },
        index=False)

    print('Creating table [wb_health_population] reference')
    tbl = db.session.query(TBL).filter_by(table_name=tbl_name).first()
    if not tbl:
        tbl = TBL(table_name=tbl_name)
    tbl.description = utils.readfile(os.path.join(DATA_FOLDER, 'countries.md'))
    tbl.main_dttm_col = 'year'
    tbl.database = utils.get_or_create_main_db()
    tbl.filter_select_enabled = True

    metrics = [
        'sum__SP_POP_TOTL', 'sum__SH_DYN_AIDS', 'sum__SH_DYN_AIDS',
        'sum__SP_RUR_TOTL_ZS', 'sum__SP_DYN_LE00_IN', 'sum__SP_RUR_TOTL'
    ]
    for m in metrics:
        if not any(col.metric_name == m for col in tbl.metrics):
            aggr_func = m[:3]
            col = str(column(m[5:]).compile(db.engine))
            tbl.metrics.append(SqlMetric(
                metric_name=m,
                expression=f'{aggr_func}({col})',
            ))

    db.session.merge(tbl)
    db.session.commit()
    tbl.fetch_metadata()

    defaults = {
        'compare_lag': '10',
        'compare_suffix': 'o10Y',
        'limit': '25',
        'granularity_sqla': 'year',
        'groupby': [],
        'metric': 'sum__SP_POP_TOTL',
        'metrics': ['sum__SP_POP_TOTL'],
        'row_limit': config.get('ROW_LIMIT'),
        'since': '2014-01-01',
        'until': '2014-01-02',
        'time_range': '2014-01-01 : 2014-01-02',
        'where': '',
        'markup_type': 'markdown',
        'country_fieldtype': 'cca3',
        'secondary_metric': 'sum__SP_POP_TOTL',
        'entity': 'country_code',
        'show_bubbles': True,
    }

    print('Creating slices')
    slices = [
        Slice(
            slice_name='Region Filter',
            viz_type='filter_box',
            datasource_type='table',
            datasource_id=tbl.id,
            params=get_slice_json(
                defaults,
                viz_type='filter_box',
                date_filter=False,
                filter_configs=[
                    {
                        'asc': False,
                        'clearable': True,
                        'column': 'region',
                        'key': '2s98dfu',
                        'metric': 'sum__SP_POP_TOTL',
                        'multiple': True,
                    }, {
                        'asc': False,
                        'clearable': True,
                        'key': 'li3j2lk',
                        'column': 'country_name',
                        'metric': 'sum__SP_POP_TOTL',
                        'multiple': True,
                    },
                ])),
        Slice(
            slice_name="World's Population",
            viz_type='big_number',
            datasource_type='table',
            datasource_id=tbl.id,
            params=get_slice_json(
                defaults,
                since='2000',
                viz_type='big_number',
                compare_lag='10',
                metric='sum__SP_POP_TOTL',
                compare_suffix='over 10Y')),
        Slice(
            slice_name='Most Populated Countries',
            viz_type='table',
            datasource_type='table',
            datasource_id=tbl.id,
            params=get_slice_json(
                defaults,
                viz_type='table',
                metrics=['sum__SP_POP_TOTL'],
                groupby=['country_name'])),
        Slice(
            slice_name='Growth Rate',
            viz_type='line',
            datasource_type='table',
            datasource_id=tbl.id,
            params=get_slice_json(
                defaults,
                viz_type='line',
                since='1960-01-01',
                metrics=['sum__SP_POP_TOTL'],
                num_period_compare='10',
                groupby=['country_name'])),
        Slice(
            slice_name='% Rural',
            viz_type='world_map',
            datasource_type='table',
            datasource_id=tbl.id,
            params=get_slice_json(
                defaults,
                viz_type='world_map',
                metric='sum__SP_RUR_TOTL_ZS',
                num_period_compare='10')),
        Slice(
            slice_name='Life Expectancy VS Rural %',
            viz_type='bubble',
            datasource_type='table',
            datasource_id=tbl.id,
            params=get_slice_json(
                defaults,
                viz_type='bubble',
                since='2011-01-01',
                until='2011-01-02',
                series='region',
                limit=0,
                entity='country_name',
                x='sum__SP_RUR_TOTL_ZS',
                y='sum__SP_DYN_LE00_IN',
                size='sum__SP_POP_TOTL',
                max_bubble_size='50',
                filters=[{
                    'col': 'country_code',
                    'val': [
                        'TCA', 'MNP', 'DMA', 'MHL', 'MCO', 'SXM', 'CYM',
                        'TUV', 'IMY', 'KNA', 'ASM', 'ADO', 'AMA', 'PLW',
                    ],
                    'op': 'not in'}],
            )),
        Slice(
            slice_name='Rural Breakdown',
            viz_type='sunburst',
            datasource_type='table',
            datasource_id=tbl.id,
            params=get_slice_json(
                defaults,
                viz_type='sunburst',
                groupby=['region', 'country_name'],
                secondary_metric='sum__SP_RUR_TOTL',
                since='2011-01-01',
                until='2011-01-01')),
        Slice(
            slice_name="World's Pop Growth",
            viz_type='area',
            datasource_type='table',
            datasource_id=tbl.id,
            params=get_slice_json(
                defaults,
                since='1960-01-01',
                until='now',
                viz_type='area',
                groupby=['region'])),
        Slice(
            slice_name='Box plot',
            viz_type='box_plot',
            datasource_type='table',
            datasource_id=tbl.id,
            params=get_slice_json(
                defaults,
                since='1960-01-01',
                until='now',
                whisker_options='Min/max (no outliers)',
                x_ticks_layout='staggered',
                viz_type='box_plot',
                groupby=['region'])),
        Slice(
            slice_name='Treemap',
            viz_type='treemap',
            datasource_type='table',
            datasource_id=tbl.id,
            params=get_slice_json(
                defaults,
                since='1960-01-01',
                until='now',
                viz_type='treemap',
                metrics=['sum__SP_POP_TOTL'],
                groupby=['region', 'country_code'])),
        Slice(
            slice_name='Parallel Coordinates',
            viz_type='para',
            datasource_type='table',
            datasource_id=tbl.id,
            params=get_slice_json(
                defaults,
                since='2011-01-01',
                until='2011-01-01',
                viz_type='para',
                limit=100,
                metrics=[
                    'sum__SP_POP_TOTL',
                    'sum__SP_RUR_TOTL_ZS',
                    'sum__SH_DYN_AIDS'],
                secondary_metric='sum__SP_POP_TOTL',
                series='country_name')),
    ]
    misc_dash_slices.add(slices[-1].slice_name)
    for slc in slices:
        merge_slice(slc)

    print("Creating a World's Health Bank dashboard")
    dash_name = "World's Bank Data"
    slug = 'world_health'
    dash = db.session.query(Dash).filter_by(slug=slug).first()

    if not dash:
        dash = Dash()
    js = textwrap.dedent("""\
{
    "CHART-36bfc934": {
        "children": [],
        "id": "CHART-36bfc934",
        "meta": {
            "chartId": 40,
            "height": 25,
            "sliceName": "Region Filter",
            "width": 2
        },
        "type": "CHART"
    },
    "CHART-37982887": {
        "children": [],
        "id": "CHART-37982887",
        "meta": {
            "chartId": 41,
            "height": 25,
            "sliceName": "World's Population",
            "width": 2
        },
        "type": "CHART"
    },
    "CHART-17e0f8d8": {
        "children": [],
        "id": "CHART-17e0f8d8",
        "meta": {
            "chartId": 42,
            "height": 92,
            "sliceName": "Most Populated Countries",
            "width": 3
        },
        "type": "CHART"
    },
    "CHART-2ee52f30": {
        "children": [],
        "id": "CHART-2ee52f30",
        "meta": {
            "chartId": 43,
            "height": 38,
            "sliceName": "Growth Rate",
            "width": 6
        },
        "type": "CHART"
    },
    "CHART-2d5b6871": {
        "children": [],
        "id": "CHART-2d5b6871",
        "meta": {
            "chartId": 44,
            "height": 52,
            "sliceName": "% Rural",
            "width": 7
        },
        "type": "CHART"
    },
    "CHART-0fd0d252": {
        "children": [],
        "id": "CHART-0fd0d252",
        "meta": {
            "chartId": 45,
            "height": 50,
            "sliceName": "Life Expectancy VS Rural %",
            "width": 8
        },
        "type": "CHART"
    },
    "CHART-97f4cb48": {
        "children": [],
        "id": "CHART-97f4cb48",
        "meta": {
            "chartId": 46,
            "height": 38,
            "sliceName": "Rural Breakdown",
            "width": 3
        },
        "type": "CHART"
    },
    "CHART-b5e05d6f": {
        "children": [],
        "id": "CHART-b5e05d6f",
        "meta": {
            "chartId": 47,
            "height": 50,
            "sliceName": "World's Pop Growth",
            "width": 4
        },
        "type": "CHART"
    },
    "CHART-e76e9f5f": {
        "children": [],
        "id": "CHART-e76e9f5f",
        "meta": {
            "chartId": 48,
            "height": 50,
            "sliceName": "Box plot",
            "width": 4
        },
        "type": "CHART"
    },
    "CHART-a4808bba": {
        "children": [],
        "id": "CHART-a4808bba",
        "meta": {
            "chartId": 49,
            "height": 50,
            "sliceName": "Treemap",
            "width": 8
        },
        "type": "CHART"
    },
    "COLUMN-071bbbad": {
        "children": [
            "ROW-1e064e3c",
            "ROW-afdefba9"
        ],
        "id": "COLUMN-071bbbad",
        "meta": {
            "background": "BACKGROUND_TRANSPARENT",
            "width": 9
        },
        "type": "COLUMN"
    },
    "COLUMN-fe3914b8": {
        "children": [
            "CHART-36bfc934",
            "CHART-37982887"
        ],
        "id": "COLUMN-fe3914b8",
        "meta": {
            "background": "BACKGROUND_TRANSPARENT",
            "width": 2
        },
        "type": "COLUMN"
    },
    "GRID_ID": {
        "children": [
            "ROW-46632bc2",
            "ROW-3fa26c5d",
            "ROW-812b3f13"
        ],
        "id": "GRID_ID",
        "type": "GRID"
    },
    "HEADER_ID": {
        "id": "HEADER_ID",
        "meta": {
            "text": "World's Bank Data"
        },
        "type": "HEADER"
    },
    "ROOT_ID": {
        "children": [
            "GRID_ID"
        ],
        "id": "ROOT_ID",
        "type": "ROOT"
    },
    "ROW-1e064e3c": {
        "children": [
            "COLUMN-fe3914b8",
            "CHART-2d5b6871"
        ],
        "id": "ROW-1e064e3c",
        "meta": {
            "background": "BACKGROUND_TRANSPARENT"
        },
        "type": "ROW"
    },
    "ROW-3fa26c5d": {
        "children": [
            "CHART-b5e05d6f",
            "CHART-0fd0d252"
        ],
        "id": "ROW-3fa26c5d",
        "meta": {
            "background": "BACKGROUND_TRANSPARENT"
        },
        "type": "ROW"
    },
    "ROW-46632bc2": {
        "children": [
            "COLUMN-071bbbad",
            "CHART-17e0f8d8"
        ],
        "id": "ROW-46632bc2",
        "meta": {
            "background": "BACKGROUND_TRANSPARENT"
        },
        "type": "ROW"
    },
    "ROW-812b3f13": {
        "children": [
            "CHART-a4808bba",
            "CHART-e76e9f5f"
        ],
        "id": "ROW-812b3f13",
        "meta": {
            "background": "BACKGROUND_TRANSPARENT"
        },
        "type": "ROW"
    },
    "ROW-afdefba9": {
        "children": [
            "CHART-2ee52f30",
            "CHART-97f4cb48"
        ],
        "id": "ROW-afdefba9",
        "meta": {
            "background": "BACKGROUND_TRANSPARENT"
        },
        "type": "ROW"
    },
    "DASHBOARD_VERSION_KEY": "v2"
}
    """)
    pos = json.loads(js)
    update_slice_ids(pos, slices)

    dash.dashboard_title = dash_name
    dash.position_json = json.dumps(pos, indent=4)
    dash.slug = slug

    dash.slices = slices[:-1]
    db.session.merge(dash)
    db.session.commit()
